Development and External Validation of a Machine Learning–Based Risk Score for Stent Outcomes in Post–Bariatric Leak Management: The “Alexandria-Bari-Stent” Tool

Background: There are no prediction models of stent outcomes for leaks after metabolic and bariatric surgery (MBS). The current study developed an artificial intelligence–based model to predict post-MBS stent failure. Methods: Prospectively maintained database of patients with post-MBS leaks was used for model development (Center I, N = 250); external validation employed patients from another hospital (Center II, N = 150). Outcome definition was failure of the first (primary/initial) stent implantation to resolve the leak, i.e., lack of primary closure. Ranking of variables was performed, 11 machine learning algorithms were tested, the best model was selected, and a stent failure point-based risk scoring system was derived, with further external validation, calibration, and decision curve analysis. Results: The development cohort (training sample, Center I) had 27.6% failed stents/72.4% successes; the external validation cohort (Center II) had 30% failures/70% successes. The Lasso logistic regression model exhibited the best performance. Eight variables contributed to the model’s predictive performance (obstructive sleep apnea, hypertension, diabetes, hepatomegaly, hyperlipidemia, body mass index, Niti-S18 stent, gastrojejunal anastomosis leak), and nine others had varying contributions (revisional surgery, Niti-S23 stent, time to stent implantation, leak size > 1 cm, age, Roux-en-Y gastric bypass surgery, esophagogastric junction leak, Hanaro 21 stent, male sex). The clinical point-based stent failure risk system showed that scores ≤ 7 had very low failure risk (<1%), scores 8–47 = low risk (1–5%), 48–77 = moderate risk (5.1–15%), 78–117 = high risk (15.1–50%), and scores ≥198 were associated with extremely high failure risk (>96%). The model’s external validation demonstrated excellent discriminatory power, distinguishing between patients with/without the outcome with 0.85 area under the ROC curve (95% CI: 0.76–0.93), 80% sensitivity (95% CI: 65.4-90.4%), 82.9% specificity (95% CI: 74.3-89.5%), and 66.7% positive predictive value (95% CI: 52.4–79.0%). The negative predictive value was 90.6% (95% CI: 82.9–95.6%) indicating that the model was particularly effective at identifying patients unlikely to fail. Area under the precision-recall curve was 0.81 (95% CI: 0.70–0.89) indicating strong performance in identifying true positives while minimizing false positives. Calibration was acceptable (Brier score = 0.15). Decision curve analysis demonstrated higher net benefit when used in clinical decision-making across a broad range of threshold probabilities (0.10–0.80) compared to treating all patients or treating none. Conclusions: A machine learning model (Alexandria-Bari-Stent) can predict post-MBS stent failure. External validation displayed high accuracy, good sensitivity/specificity, and excellent negative predictive value indicating good discriminative ability. Clinically, the model is more reliable for ruling out stent failure than confirming it, making it especially useful in reassuring low-risk post-MBS leakage patients. Patient’s general status, metabolic health, and systemic factors appeared to play a more critical role than previously recognized, complementary to, not in conflict with, established technical and local factors that influence successful stent outcomes for leak management. This prompts the need for a more holistic view of leak patients who are candidates for stenting. Prospective multicenter trials are needed to confirm the performance of the Alexandria‑Bari‑Stent model and the role of metabolic stabilization and medically optimizing the patient for better outcomes.